# `emcuve_select` — EMCUVE — Ensemble MC-UVE
_Group_: **Variable selector** · _Registry tolerance_: `1e-06`
## Description
EMCUVE ensemble MC-UVE (§18 Phase 5n)
From the `pls4all.sklearn.EMCUVESelector` docstring:
> Ensemble Monte-Carlo UVE selector.
> **Registry note** — R `plsVarSel::mcuve_pls` called `n_ensembles` times with deterministic seeds (`11 + e`) and vote-aggregated. Default `_emcuve_select_pls4all` path mirrors the same R loop, giving bit-exact mask parity. The C++ splitmix64 EMCUVE kernel is opt-in via `legacy=True`.
### Parameters
| Name | Type | Default | Notes |
|------|------|---------|-------|
| `n_components` | `int` | `2` | Number of latent components extracted (k). |
| `noise_features` | `int` | `50` | Number of artificial noise variables appended to X for the UVE threshold. |
| `noise_seed` | `int` | `0` | Seed for the appended noise variables. |
| `n_ensembles` | `int` | `10` | Number of UVE replicates aggregated by majority vote. |
| `vote_threshold` | `float` | `0.5` | Minimum vote fraction required to retain a variable in EMCUVE. |
| `n_folds` | `int` | `3` | Number of cross-validation folds used inside the selector. |
## Explanations
### Bibliographic source
Han, Q.-J., Wu, H.-L., Cai, C.-B., Xu, L. & Yu, R.-Q. (2008). *An ensemble of Monte Carlo uninformative variable elimination for wavelength selection*. Analytica Chimica Acta 612(2), 121–125. https://doi.org/10.1016/j.aca.2008.02.032 — extends the MC-UVE procedure of Cai et al. (2008) (`stability_select`) by aggregating independent MC-UVE rounds through a vote rule.
### Mathematical principle
Run multiple independent MC-UVE rounds with different seeds, threshold each independently, then **vote** across rounds: a feature is selected if it survives thresholding in a majority of rounds. Robust against single-round instability caused by particular bootstrap samples.
The voting rule has a free parameter (majority threshold); the default of $\lceil R/2 \rceil$ is the median-style majority. Stricter thresholds give smaller but more reliable subsets.
### Implementation
`n4m_emcuve_select`.
### Usage
Every pls4all binding tab dispatches into the same C kernel; the external libraries listed at the bottom of the page are the parity references registered in `benchmarks.parity_timing.registry`. Switch tabs to read the same fit in your language. The R package now ships drop-in-compatible facades for the CRAN `pls` package (`plsr`, `pcr`, `mvr`) and for the `mdatools::pls(x, y, ...)` matrix idiom — those tabs appear only on the methods that have a meaningful equivalence.
**pls4all bindings**
::::{tab-set}
:class: pls4all-bindings
:::{tab-item} C ABI · libn4m
:sync: c
:class-label: lang-c
```c
/* C ABI — libn4m */
n4m_context_t* ctx = n4m_context_create();
n4m_config_t* cfg = n4m_config_create();
n4m_method_result_t* res = NULL;
n4m_emcuve_select_fit(ctx, cfg, &x_view, &y_view, /* hyperparams */, &res);
/* … read coefficients / mask / scores via */
/* n4m_method_result_get_double_matrix / vector / scalar … */
n4m_method_result_destroy(res);
n4m_config_destroy(cfg);
n4m_context_destroy(ctx);
```
:::
:::{tab-item} Python · pls4all (raw)
:sync: python-raw
:class-label: lang-python
```python
import pls4all
from pls4all._methods import emcuve_select_fit
with pls4all.Context() as ctx, pls4all.Config() as cfg:
res = emcuve_select_fit(ctx, cfg, X, y, n_components=4)
# then: res.matrix("predictions"), res.matrix("coefficients"),
# res.vector("mask"), res.scalar("intercept"), …
```
:::
:::{tab-item} Python · pls4all.sklearn
:sync: python-sklearn
:class-label: lang-python
```python
from pls4all.sklearn import EMCUVESelector
mdl = EMCUVESelector(n_components=2, noise_features=50, noise_seed=0, n_ensembles=10, vote_threshold=0.5, n_folds=3)
mdl.fit(X, y)
y_hat = mdl.predict(X_test)
```
:::
:::{tab-item} R · pls4all_method()
:sync: r-dispatcher
:class-label: lang-r
```r
library(pls4all)
# Unified low-level dispatcher (May 2026 R cleanup):
res <- pls4all_method("emcuve_select", X, y,
n_components = 4L, params = list(noise_features = 5L, n_ensembles = 5L, vote_threshold = 0.5, noise_seed = 11L))
# res is a named list with MethodResult arrays/scalars.
# selected_indices / top_k_intervals are 1-based.
```
:::
:::{tab-item} MATLAB · pls4all (MEX)
:sync: matlab-mex
:class-label: lang-matlab
```matlab
res = pls4all.fit("emcuve_select", X, y, "NumComponents", 4);
yhat = predict(res, Xtest);
```
:::
:::{tab-item} MATLAB · pls4all (classdef)
:sync: matlab-classdef
:class-label: lang-matlab
_No idiomatic classdef wrapper — invoke `pls4all.fit("emcuve_select", X, y, …)` directly from the unified MEX factory._
:::
::::
**Registry parity references** 📐
:::{card}
:class-card: external-refs
- 📐 **`ref.r_plsvarsel`** (R · r) — `plsVarSel` 0.10.0 · strict (rmse_rel ≤ 1e-06) — R `plsVarSel::mcuve_pls` repeated N times with different seeds, then vote-aggregated. Same algorithm family as pls4all's EMCUVE. RNGs differ; mask metric ~0=perfect.
:::
### Benchmarks
Adaptive wall-clock per cell measured against [`full_matrix.csv`](../benchmarks/overview.md). Only backends that implement this method are listed; libraries without the method are omitted.
**Verdict** · ✓ ref / ≈ ref / ~ shape mark a reference-gate pass at strict / relaxed / qualitative tolerance · ✓ bind = pls4all binding agrees with the C++ baseline · ✗ divergent · ⚠ error · — not run. The fastest backend per column is marked 🏆.
**Reference gate**: strict — numeric equivalence (`rmse_rel_tol ≤ 1e-06`).
Rows tagged with **📐** are the canonical parity references for this method (declared in [`parity_timing.registry`](../benchmarks/methodology.md)). C++ and external rows show reference parity; pls4all language bindings show binding parity against the C++ backend. Hover the icon for role and tolerance band.
::::{tab-set}
:class: parity-tabs
:::{tab-item} 1 thread
:sync: threads-1
| Backend | Parity | 50×250 (ms) | 100×50 (ms) | 100×500 (ms) | 100×2500 (ms) | 200×40 (ms) | 250×50 (ms) | 500×50 (ms) | 500×500 (ms) | 500×2500 (ms) | 2500×50 (ms) | 2500×500 (ms) | 2500×2500 (ms) | 10000×50 (ms) | 10000×500 (ms) |
|---|
| C++ native · libn4m |
|---|
pls4all.cpp.blas | ≈ | 951.2 ms | 5.2 s🏆 | 18.9 s | 119.3 s | 1.7 s | 2.3 s | 33.8 s🏆 | 541.2 s | — | — | — | — | — | — |
pls4all.cpp.blas+omp | ≈ | 962.2 ms | 5.4 s | 18.0 s🏆 | 110.7 s🏆 | 1.7 s | 2.2 s | 36.7 s | 506.0 s🏆 | — | — | — | — | — | — |
pls4all.cpp.omp | ≈ | 956.3 ms | 5.6 s | 19.4 s | 112.4 s | 1.7 s | 2.3 s | 34.0 s | 530.6 s | — | — | — | — | — | — |
pls4all.cpp.ref | ≈ | 946.1 ms | 5.3 s | 19.0 s | 117.9 s | 1.7 s | 2.3 s | 35.6 s | 519.1 s | — | — | — | — | — | — |
| Python · pls4all |
|---|
pls4all.python | ✓ bind | 1.0 s | — | — | — | 1.7 s | 2.3 s | — | — | — | — | — | — | — | — |
pls4all.sklearn | ✗ +1e+00 | 3.80 ms | — | — | — | 3.05 ms🏆 | 3.26 ms | — | — | — | — | — | — | — | — |
| R · pls4all |
|---|
pls4all.R | ✗ +1e+00 | 14.0 ms | — | — | — | 6.82 ms | 12.0 ms | — | — | — | — | — | — | — | — |
pls4all.R.formula | ✗ +1e+00 | 20.5 ms | — | — | — | 9.00 ms | 10.8 ms | — | — | — | — | — | — | — | — |
pls4all.R.mdatools | ✗ +1e+00 | 21.0 ms | — | — | — | 8.48 ms | 14.3 ms | — | — | — | — | — | — | — | — |
pls4all.R.pls | ✗ +1e+00 | 22.0 ms | — | — | — | 8.28 ms | 14.0 ms | — | — | — | — | — | — | — | — |
| MATLAB · pls4all |
|---|
pls4all.matlab | ✗ +1e+00 | 4.94 ms | — | — | — | 3.26 ms | 4.96 ms | — | — | — | — | — | — | — | — |
pls4all.matlab.classdef | ✗ +1e+00 | 5.55 ms | — | — | — | 3.66 ms | 6.63 ms | — | — | — | — | — | — | — | — |
| R · external |
|---|
📐ref.r_plsvarsel | source | 533.2 ms🏆 | — | — | — | 1.1 s | 2.0 s🏆 | — | — | — | — | — | — | — | — |
:::
:::{tab-item} 3 threads
:sync: threads-3
| Backend | Parity | 50×250 (ms) | 100×50 (ms) | 100×500 (ms) | 100×2500 (ms) | 200×40 (ms) | 250×50 (ms) | 500×50 (ms) | 500×500 (ms) | 500×2500 (ms) | 2500×50 (ms) | 2500×500 (ms) | 2500×2500 (ms) | 10000×50 (ms) | 10000×500 (ms) |
|---|
| C++ native · libn4m |
|---|
pls4all.cpp.blas | ✓ ref | — | — | — | — | 1.4 s | — | — | — | — | — | — | — | — | — |
pls4all.cpp.blas+omp | ✓ ref | — | — | — | — | 1.4 s | — | — | — | — | — | — | — | — | — |
pls4all.cpp.omp | ✓ ref | — | — | — | — | 1.4 s | — | — | — | — | — | — | — | — | — |
pls4all.cpp.ref | ✓ ref | — | — | — | — | 1.4 s | — | — | — | — | — | — | — | — | — |
| Python · pls4all |
|---|
pls4all.python | ✓ bind | — | — | — | — | 1.5 s | — | — | — | — | — | — | — | — | — |
pls4all.sklearn | ✓ bind | — | — | — | — | 2.12 ms🏆 | — | — | — | — | — | — | — | — | — |
| R · pls4all |
|---|
pls4all.R | ✗ +1e+00 | — | — | — | — | 6.69 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.formula | ✗ +1e+00 | — | — | — | — | 7.17 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.mdatools | ✗ +1e+00 | — | — | — | — | 7.21 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.pls | ✗ +1e+00 | — | — | — | — | 7.42 ms | — | — | — | — | — | — | — | — | — |
| MATLAB · pls4all |
|---|
pls4all.matlab | ✗ +1e+00 | — | — | — | — | 2.92 ms | — | — | — | — | — | — | — | — | — |
pls4all.matlab.classdef | ✗ +1e+00 | — | — | — | — | 5.42 ms | — | — | — | — | — | — | — | — | — |
| R · external |
|---|
📐ref.r_plsvarsel | source | — | — | — | — | 1.0 s | — | — | — | — | — | — | — | — | — |
:::
:::{tab-item} 10 threads
:sync: threads-10
| Backend | Parity | 50×250 (ms) | 100×50 (ms) | 100×500 (ms) | 100×2500 (ms) | 200×40 (ms) | 250×50 (ms) | 500×50 (ms) | 500×500 (ms) | 500×2500 (ms) | 2500×50 (ms) | 2500×500 (ms) | 2500×2500 (ms) | 10000×50 (ms) | 10000×500 (ms) |
|---|
| C++ native · libn4m |
|---|
pls4all.cpp.blas | ✓ ref | — | — | — | — | 1.2 s | — | — | — | — | — | — | — | — | — |
pls4all.cpp.blas+omp | ✓ ref | — | — | — | — | 1.3 s | — | — | — | — | — | — | — | — | — |
pls4all.cpp.omp | ✓ ref | — | — | — | — | 1.3 s | — | — | — | — | — | — | — | — | — |
pls4all.cpp.ref | ✓ ref | — | — | — | — | 1.3 s | — | — | — | — | — | — | — | — | — |
| Python · pls4all |
|---|
pls4all.python | ✓ bind | — | — | — | — | 1.2 s | — | — | — | — | — | — | — | — | — |
pls4all.sklearn | ✓ bind | — | — | — | — | 1.98 ms🏆 | — | — | — | — | — | — | — | — | — |
| R · pls4all |
|---|
pls4all.R | ✗ +1e+00 | — | — | — | — | 4.55 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.formula | ✗ +1e+00 | — | — | — | — | 5.73 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.mdatools | ✗ +1e+00 | — | — | — | — | 5.47 ms | — | — | — | — | — | — | — | — | — |
pls4all.R.pls | ✗ +1e+00 | — | — | — | — | 5.62 ms | — | — | — | — | — | — | — | — | — |
| MATLAB · pls4all |
|---|
pls4all.matlab | ✗ +1e+00 | — | — | — | — | 2.66 ms | — | — | — | — | — | — | — | — | — |
pls4all.matlab.classdef | ✗ +1e+00 | — | — | — | — | 2.97 ms | — | — | — | — | — | — | — | — | — |
| R · external |
|---|
📐ref.r_plsvarsel | source | — | — | — | — | 895.4 ms | — | — | — | — | — | — | — | — | — |
:::
::::
---
_See also_: [benchmark overview](../benchmarks/overview.md) · [methods index](index.md) · [interactive dashboard](../landing/dashboard.md)